Analysis of E-Commerce Product Sales Prediction Based on Ga-Bp Neural Network
DOI:
https://doi.org/10.54097/hbem.v5i.5124Keywords:
GA-BP optimization neural network, accuracy evaluation index, electronic goods.Abstract
In recent years, the market for the sale of various goods has been expanding and with it comes huge amounts of data that needs to be specifically processed and analyzed. For a company, the adoption of artificial intelligence algorithms with better performance in handling big data is an important step in influencing the future decisions and future growth of the company. In this paper, an optimized neural network based on GA-BP is constructed to analyze samples with different characteristics to predict the future sales in the short term. After testing, the prediction results are found to be good. First, the degree of influence between the indicators is examined using BP neural network. By comparing whether the inventory indicator at the beginning of the month is used as the model input, the error of the end-of-month inventory forecast content is obtained, which leads to the conclusion that the inventory at the beginning of the month has a greater impact on the inventory at the end of the month. Similarly, it can be concluded that the commodity type has a greater impact on the commodity pool. Second, to further analyze the relationship between commodity type and commodity inventory, the Eta test was used. It was found that item type A2 had the greatest impact on merchandise inventory, followed by type A1 and finally type A3. This finding is consistent with the characteristics of the dataset. Considering that the optimized GA-BP neural network model is mainly used to predict merchandise sales in the next 3 months; secondly, considering the specificity of some data in the sample, it is not suitable to use the neural network for analysis and prediction. For all data, the data that could be analyzed by GA-BP were found by gradient descent method, and it was found that the data with target value below 5500 had the best prediction effect. Finally, according to the given accuracy evaluation index, the score of the model is calculated as 0.678 5, and the model works well.
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